Helmholtz Institute Freiberg for Resource Technology
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Measurement of the axial gas dispersion coefficient in bubble columns of several diameters via gas flow modulation
No full textThis dataset was aquired during gas flow modualtion experiments for determining the axial gas dispersion coefficient in bubble columns. The applied measurement technique is gamma-ray densitometry and the dataset consists of densitometry measurements at several axial positions in the bubble columns. Columns of 100, 150 and 330 mm internal diameter were tested. The 100 mm ID column was tested with three different gas spargers to investigate the effect of the gas distributor on gas dispersion. Several operating conditions were tested inside of the homogenous flow regime.
Please refer to the attached Excel for details of single files.This research was financially supported by DFG, grant HA 3088/18-
VACVPlaque: mobile photography of Vaccinia virus plaque assay with segmentation masks
No full textHow to Cite Us
De, T., Thangamani, S., Urbański, A., & Yakimovich, A. (2025). A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning. Scientific Data, 12(1), 719.
@article{de2025digital,
title={A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning},
author={De, Trina and Thangamani, Subasini and Urba{\'n}ski, Adrian and Yakimovich, Artur},
journal={Scientific Data},
volume={12},
number={1},
pages={719},
year={2025},
publisher={Nature Publishing Group UK London}
}
Data Description
The VACVPlaque dataset comprises spatially correlated objects, specifically virological plaques, which are circular phenotypes indicative of vaccinia virus (VACV) spread, and the wells of the assay plate. The virus plaque assay is a common method performed by infecting a monolayer of host cells (indicator cells) that are grown in the wells of assay plates or dishes. The host cells are infected with varying concentrations of a highly diluted virus inoculum. After an incubation period, typically around 48 hours, the cells are fixed with formaldehyde and stained with a dye to reveal the plaques or areas of cell death. By counting these plaques, researchers can calculate the number of infectious particles present in the original inoculum as described in [1].
This dataset consists of mobile photographs of 6-well tissue culture plates where the VACV plaque assay was conducted. The photographs were taken using two different mobile phones, resulting in 211, 8-bit RGB images with a resolution of 2448 x 3264 pixels. Each plate was photographed from two different perspectives using two different devices, meaning there are two images of the same plate but from different angles and devices.
To aid in the training of machine learning models, the dataset is divided into training, validation, and test subsets in a 70:20:10 ratio. To prevent data leaks, only one perspective of each image is included in the validation and test subsets. The training subset, which includes images from both perspectives, consists of 148 images.
File Description:
VACVPlaque_train.zip -> train holdout
VACVPlaque_validation.zip -> validation holdout
VACVPlaque_test.zip -> test holdout
Each zip file contains:
images -> {filename}.tif
plaque_masks -> {filename}.tif
well_masks -> {filename}.tif
References:
1. Dulbecco, Renato. "Production of plaques in monolayer tissue cultures by single particles of an animal virus." Proceedings of the National Academy of Sciences 38, no. 8 (1952): 747-752
Data publication: Ab initio Density Response and Local Field Factor of Warm Dense Hydrogen
No full textThis repository contains all PIMC results related to the publication "Ab initio Density Response and Local Field Factor of Warm Dense Hydrogen". Generally, data formats are identical to figures. Exceptions are 3D ITCF data sets for Figs. 2, 8 and 12: #1 k [a_Bohr^{-1}], #2 tau [Ha^{-1}], #3/#4 F(q,tau)x32 and statistical error and the "ITCF" folders with the raw data for F(q,tau): ITCF: #1 tau [Ha^{-1}]; #2/3: F(q,tau) and statistical error The number after "index" in the file names gives the number of the respective q-vector; see "static_structure_factor_key.dat", columns 1 and 2 for the respective index-to-q mapping, with [q]=a_Bohr{-1
HeLaCytoNuc: fluorescence microscopy dataset with segmentation masks for cell nuclei and cytoplasm
No full textData Description:
This dataset comprises fluorescence micrographs of HeLa cells, specifically labelled to identify nuclei and cell cytoplasm. These images were acquired as a technical calibration for a high-content screening study detailed and published in [1].
The HeLa cell line (ATCC-CCL-2), a widely used immortalised cell line in laboratory research, was cultured under standard conditions. Post-cultivation, the cells were fixed and stained with fluorescent dyes to visualise the nuclei and cytoplasm. The nuclei were stained with DAPI (4',6-diamidino-2-phenylindole), a blue-fluorescent DNA stain, while fluorescent-labeled phalloidin was used to detect actin filaments and delineate the cytoplasm. The entire process of cell culture, fixation, staining, and imaging adhered strictly to the protocols described in [1].
The preprocessed dataset includes 2,676 8-bit RGB images, each with a pixel resolution of 520 x 696 pixels. In these images, only two of the RGB channels are utilized: the red channel represents the cytoplasm, and the blue channel represents the nuclei. The dataset is divided into training, validation, and test subsets in a 70:20:10 ratio. The entire dataset is accompanied by instance segmentation masks for nuclei and cytoplasm objects obtained through a specialised CellProfiler [2] software. Notably, the test subset was annotated manually by a specialist, ensuring high-quality annotations. The original raw images are of a higher resolution, 1040 x 1392 pixels, and have a bit depth of 16 bits, providing more detailed information for advanced analyses.
File Description:
The file structure of the zip files is as follows:
HeLaCytoNuc_{train/validation/test}.zip ->
- images -> {filename}.tif
- nuclei_masks -> {filename}.tif
- cytoplasm_masks -> {filename}.tif
HeLaCytoNuc_raw_images.zip -> {filename}.tif
HeLaCytoNuc_test_cellprofiler_masks.zip ->
- nuclei_masks -> {filename}.tif
- cytoplasm_masks -> {filename}.tif
References:
1. Rämö, Pauli, Anna Drewek, Cécile Arrieumerlou, Niko Beerenwinkel, Houchaima Ben-Tekaya, Bettina Cardel, Alain Casanova et al. "Simultaneous analysis of large-scale RNAi screens for pathogen entry." BMC genomics 15 (2014): 1-18.
2. Carpenter, Anne E., Thouis R. Jones, Michael R. Lamprecht, Colin Clarke, In Han Kang, Ola Friman, David A. Guertin et al. "CellProfiler: image analysis software for identifying and quantifying cell phenotypes." Genome biology 7 (2006): 1-11
Self-folding of two-dimensional thin templates into pyramidal micro-structures by a liquid drop - a numerical model
No full textSource files and selected raw data related to the manuscript "Self-folding of two-dimensional thin templates into pyramidal micro-structures by a liquid drop - a numerical model" by Gregory Lecrivain, Helmholtz-Zentrum Dresden-Rossendorf, Germany, 2024.
1) folder "manuscript",
This folder contains all text documents related to manuscript. Text and final figures are found in the directory.
2) folder "scripts"
This folder contains python and bash scripts used to post-process the raw data and prepare the figures. You will need to install some python3 libraries. Use the following command: pip install pyquaternion matplotlib scipy intersect.
3) folder "figures"
This folder contain information on how to run the simulations related to the figure. More information can be found in the README text file located in each figure/figX subfolder, where X the figure number in the manuscript.
4) folder "src"
This folder contains the all c++ files related to the source code.
4.1)
Prior to compiling, you should have gcc(7.3.0), openmpi(2.1.2), make(4.3), cmake(3.20.2), python(3.8.0), blas(3.8.0), lapack(3.8.0), boost(1.78.0), and git(2.30.1) available on your machine. The version number in the parenthesis corresponds to the one I used on the local HPC available at my institution. In my case, I type "module load gcc/7.3.0 openmpi/2.1.2 make/4.3 cmake/3.20.2 python/3.8.0 blas/3.8.0 lapack/3.8.0 boost/1.78.0 git/2.30.1".
4.2)
To compile the libraries, open a terminal, cd to the src directory and type "make libs". All outputs will placed in the folder LD_LIBRARY_PATH:LD_LIBRARY_PATH:LD_LIBRARY_PATH:PATH:HOME/microorigami/src #(or whereever, your chosen parent directory is)
export PATH=PATH:HOME/microorigami/scripts #(or whereever, your chosen parent directory is)
export PATH=PATH:$HOME/microorigami/paraview/bin #(or whatever path you used)
4.5)
open a new terminal, cd to the src directory and type "make check_library_path". The terminal should return
"library path to libconfig is correct"
"library path to gmp is correct"
"library path to mpfr is correct"
If that is the case, i.e. the paths are correctly set. To compile, type "make main post". Alternatively, one can speed up the installation by typing "make -j 4 main post", where 4 is the number of cpus I use.
4.6)
Help is available in each header file (.h) in the form of doxygen comments. Type "make doxy". The folder html will appear under src.
4.7)
Type "make clean" to clean the src folder
5) folders "caX_sideY_ecZ.zip"
The zip files contains the raw data related to Figure 10. Here, X = 70 is the contact angle, Y = 5 the number of side panels and Z = 0.8, 1.6 and 2.4 the elasto-capillary number. After data extraction, three folders will be created, namely wd/ca70/side5/ec0.8, wd/ca70/side5/ec1.6 and wd/ca70/side5/ec2.4, where wd is your working directory. To convert the data into human-readable format (txt, vtk, stl,...) type "source Utils.sh; ExportScript --verbose --submit" in the working directory wd on the hpc. The bash function ExportScript is located in "scripts/Utils.sh". All other raw data can be obtained by following the commands in the README text file located in each figX folder, with X=1,2,...,13. With Paraview, one is able to visualize the self-folding by loading the stl files
Data publication: Programmable activation of quantum emitters in high-purity silicon with focused carbon ion beams
No full textExperimenta dat
Test data for MALA
No full textThis repository contains data to test, develop and debug MALA and MALA based runscripts. If you plan to do machine-learning tests ("Does this network implementation work? Is this new data loading strategy working?"), this is the right data to test with. It is NOT production level data
Data publication: An approach for in situ fouling monitoring in heat exchangers using electrical impedance spectroscopy
No full textThe dataset presented in this study is focused on the in situ monitoring of organic fouling in a plate heat exchanger using electrical impedance spectroscopy (EIS). The primary objective was to accurately determine the thickness of fouling layers that develop over time during heat exchanger operation. The experiments were conducted using an impedance analyzer (Sciospec ISX-3) configured in a four-terminal setup with two electrodes. The counter and reference electrodes were connected to a probe positioned at the top of the chamber, while the working and sensing electrodes were connected to the heating plate of the heat exchanger. This configuration allowed for the precise measurement of impedance across the fouling layer. The amplitude of the excitation signal was set to 1 V (rms). This value was chosen to maintain linearity at high frequencies and to ensure a high signal-to-noise ratio (SNR). The impedance spectra were recorded across a frequency range of 10 Hz to 2 MHz. The spectrum included 50 measurement points that were logarithmically spaced within this range to capture detailed impedance characteristics across different frequencies. Impedance data were sampled at a rate of 45 mHz, and measurements were collected over a duration of approximately 600 minutes, allowing for continuous monitoring of the fouling development process. The raw data consists of impedance measurements, reflecting changes in the electrical properties of the fouling layer as it accumulates on the heat exchanger surfaces. The data points in the spectrum provide insights into the relationship between fouling thickness and impedance across various frequencies, which is critical for developing models to predict fouling behavior in heat exchangers.This dataset is valuable for researchers and engineers interested in non-invasive fouling monitoring techniques, offering a foundation for improving heat exchanger efficiency through real-time fouling detection and characterization
Data publication: Ultrafast unidirectional spin Hall magnetoresistance driven by terahertz light field
No full textRaw data for the publication titled 'Ultrafast Unidirectional Spin Hall Magnetoresistance Driven by a Terahertz Light Field,' including the data presented in Figures 2 through 4
Data publication: Exploiting Deep Learning Models for Iron Ore Particle Size Estimation in the Primary Crusher Input
No full textThe data contains the analysis results of the research work